In modern society, automobiles are becoming increasingly widespread with the rapid economic growth, which imposes more and more pressures on urban traffic and causes increasingly severe traffic jams. It is advantageous to mitigate traffic congestions, so as to reduce travel time for automobile drivers, reduce fuel consumption, improve economic efficiency of a city and facilitate environment protection. Thus, the traffic information service system plays an important role in urban intelligent traffic system.
With respect to traffic information gathering, the current rapid development of multi-media technology, mobile communication technology and the popularization of GPS technology provide great potentials for traffic information services. In traffic information gathering, stationary probing devices deployed along the roads, such as cameras, loops and RTMS (Remote Traffic Microwave Sensor), can accurately gather data for traffic information, which is, however, limited to arterial road network in general. The probe vehicle technology, which mainly uses taxies, can calculate traffic information for urban road network in real time, but it is subjected to objective constraints such as the number of probe vehicles. An information gathering personnel is capable of uploading observed traffic information as a text to a data center through a simple mobile communication device. In this case, however, information is limited in amount and also is inaccurate. A user uploading approach, in which a driver uploads traffic information for the area where he/she is currently located to a data center via a channel provided by a mobile information service provider, suffers from limited coverage. In summary, there has been a diversity of approaches for gathering traffic data. However, these approaches have different types of data formats, different description fashions and respective drawbacks in information completeness and accuracy. An effective approach for improving accuracy of traffic information and enlarging coverage is to represent traffic information data from different sources by a universal traffic information describing model, and thus to take advantages of different data sources and fuse traffic information from various data sources for supplementing each other. A traffic information describing model featured by text description can play a greater role in gathering and mining of traffic information data.
With respect to traffic information distribution, traditional distribution approaches are based on traffic information billboards and in-vehicle navigators. With the diversification of communication approaches and the development of information services, real-time traffic information becomes available at portal websites which then provide short message services and pictorial and/or textual prompts which will be ubiquitous in the future. A question-and-answer or interactive automatic traffic information service system is also desirable. Thus, a traffic information model supporting a number of types of presentation terminals, which supports both graphical display of navigation maps or man-made diagrams and understandable text information services, becomes increasingly important.
In all, a good and universal traffic information describing model should be compliant with traffic information description convention in people's daily life, capable of describing critical traffic information elements and establishing the relationship between the text description for these elements and geographical space. In a traffic information describing model, the text description of the traffic information elements and the correspondence between the elements and geographical space are very important for supporting fusion and conversion between text description-based traffic information and geographical space-based traffic information as well as information presentation on various terminals.
In practical applications, traffic data are generally based on digital navigation map data and text information. The digital navigation map data is primarily aimed at providing navigation road network and contains very detailed topological information on road network. Such topological information uses link and node as basic units. Herein, link is an arc in a road topological network, which is a segment of a road; while node is a vertex in a road topological network where neighboring links are connected. Stationary probing technologies, such as loops and cameras, and mobile probing technologies, such as probe vehicle, are mostly based on digital navigation map data, for which the calculated traffic information is described in units of link travel speed or travel time. However, such data structure is not designed for traffic information service and unable to define text description attributes for traffic information and to include the relationship between traffic information describing objects and links/nodes. On the other hand, traffic information in a text form is described in daily language and used for person-to-person communication. It has no relation established with links in the digital navigation map and cannot be used for driving navigation directly. As an example, a traffic information description of “Intersections with Road B and Road C on Road A are congested, with a speed of 10 km/h” is text description-based information, which is easily understandable for oral communication but cannot be used for navigation services directly. Additionally, there is no correspondence between intersections/road sections and links/nodes in the digital navigation map data. In contrast, the traffic information collected by the probe vehicle technology is based on link travel speed, which cannot be notified to the end user before being converted into text description-based traffic information description.
Some of the existing patent and non-patent documents relate to methods and models for describing traffic information. Most of these methods and models, however, only relate to how to map text description-based traffic information onto a road network such as digital navigation map, or only involve combination and fusion of traffic information which is based on a large amount of links each having a short length. They are only directed to solve some local or one-way conversion problems, but fail to establish an intermediate model between text description-based traffic information and link-based traffic information. Such intermediate model is essential for describing traffic information in our daily language. This model is a kernel, easily understandable model which can correspond to various forms of data sources for traffic information. Some related prior art patents and papers will be introduced in the following.
Patent Document 1 (CN 101308487A), “A Spatio-Temporal Fusion Method for Describing Dynamic Traffic Information in Natural Language”, discloses a processing method for converting traffic information in natural language into traffic information based on a road topological network on an digital navigation map. At first, the traffic information in natural language is separated into location names, such as road names and bridge names, and their traffic conditions. These location names are then matched to geographical objects in the digital navigation map. In this case, a point, a path or nothing can be matched. Then a path among the matched results can be found, which is a geographical space traffic description corresponding to the traffic information in natural language.
Patent Document 2 (US20060111833A1), “Method and System for modeling and processing vehicle traffic data and information and applying thereof”, discloses a method and system for modeling and processing traffic data and information. This document discloses the concept of directional road segment, i.e., a path segment between two intersections on a digital navigation map, which is used for fusing traffic data from various sources.
Non-Patent Document 1, “Macroscopic Structural Summarization of Road Networks for Mobile Traffic Information Services”, published on the 7th International Conference on Mobile Data Management, 2006, proposes a method for simplifying road structures for mobile traffic information service. The complicated road topological network on the digital navigation map is combined, regulated and transformed into a simple, distorted, brief structural map.
Non-Patent Document 2, “A Map Ontology Driven Approach to Natural Language Traffic Information Processing and Services”, published on the 1st Annual Asian Semantic Web Conference, 2006, proposes a geographical ontology model for traffic information processing and services. From the perspective of end users, this model defines geographical ontologies, for describing traffic information such as roads and sections, and the correspondence among the ontologies. This approach is mainly used for natural language processing in the field of traffic information.
Among the related solutions as mentioned above, the solution of Patent Document 1 is only capable of converting text description-based traffic information into geographical space-based traffic information, but not vice versa. Moreover, this solution is inaccurate, computationally consuming and based on necessary premises that information such as location names, bridge names, sections and intersections is included in the digital navigation map and that the operation of path matching can find a unique, correct path. As noted above, the digital navigation map is designed for path navigation, with its kernel being road topological network, but fails to fully consider roads, locations and the like involved in traffic information. Meanwhile, path matching usually result in a number of path options, from which it is difficult to determine which of the matched paths to be selected. Thus, this solution is only capable of converting text description-based information into geographical space-based traffic information with a low matching rate and high computational cost. The solution of Patent Document 2 only considers fusion but fails to consider how to provide text description-based, easily understandable traffic information for information distribution. Besides, it completely ignores intersection as a critical traffic information element. The solution of Non-Patent Document 1 can provide better user experience by graphically distributing traffic information, but cannot provide traffic information description in text. Additionally, Non-Patent Document 2 does not account for the correspondence between geographical ontology of traffic information and geographical space.
In all, the existing traffic information describing models have their respective drawbacks. They are incapable of establishing a traffic information describing model from a universal, reasonable and efficient perspective and globally considering the description of traffic information, including the traffic information elements to be defined, the relationship between these elements and the relationship between these elements and geographical space.
Thus, it is desired to establish a universal traffic information element describing model which is compliant with our daily usage of language and based on common traffic elements, such as roads, intersections, sections and the like. From the perspective of real applications, such a model can establish correspondence between these elements and the road topological network on digital navigation map, such that a two-way conversion between the traffic description information in text and the traffic information represented with the road topological network on digital navigation map is made possible. With this model, traffic information from various data sources can be integrated and various forms of traffic information can be distributed.